Instructions to use Hartunka/bert_base_rand_100_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hartunka/bert_base_rand_100_v1 with Transformers:
# Load model directly from transformers import AutoTokenizer, DistilBertForLDAMaskedLM tokenizer = AutoTokenizer.from_pretrained("Hartunka/bert_base_rand_100_v1") model = DistilBertForLDAMaskedLM.from_pretrained("Hartunka/bert_base_rand_100_v1") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 25.0, | |
| "total_flos": 1.5161929390646784e+18, | |
| "train_loss": 8.295239665226012, | |
| "train_runtime": 24715.4683, | |
| "train_samples": 228639, | |
| "train_samples_per_second": 231.271, | |
| "train_steps_per_second": 2.409 | |
| } |